machine learning 101
TRANSCRIPT
Machine Learning 101Setu ChokshiCommunity Technology Update 2016
Machine Learning 101
This is an introductory session and we are here to learnFeel free to ask questions at any time
Why?Discover reason behind success, failureUnderstand customers, productsPlan futureExperiment meaningfullyImprove performance
Run on analytics
Data science
Scientific method of reasoning applied to data-driven decisions
Hypothesis, experiments, facts, logical reasoning+ data engineering.
Data wrangling (munging), retrieval
+ storage
Data mining & machine learning
Statistics
Big data
Data scienc
e
Machine learning ≣ data mining
Exploresdata
Finds patterns
Predicts (scoring)
Means strictly equivalent to. Yes.
Tools
Tools & salaries
Chart from "2016 Data Science Salary Survey"
Examples of Machine Learning
How does machine learning help?There are only 5 questions that machine learning can help answer
Source: Data Science For Beginners - 5 Questions Data Science Answers by Brandon Rohrer
1. Is this A or B?
Is this A or B?Classification Algorithms
2. Is this Weird?
Is this Weid?Anomaly detection algorithms
3. How much? How many?How many?How much?
Regression algorithms
4. How is this organized?How is this organized?
Clustering algorithms
5. What should I do now?What should I do
now?
Reinforcement learning algorithms
Machine learning process and algorithms
How does it workAlgorithm
Your data
Computer
Your answer
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Recipe
Ingredients
Blender
Smoothie
1. Define & initialise a model2. Train model (process cases)3. Validate model
…by scoring (making predictions) a test data set and evaluating the results
4. Use it: Explore or Deploy…visualise and study…deploy as a (web) service
5. Update and revalidate
How?
Cheat Sheet
http://download.microsoft.com/download/A/6/1/A613E11E-8F9C-424A-B99D-65344785C288/microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf
Classification
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ClassificationThe classification model can be implemented in several ways• Decision trees• Rules• Mathematical formulae
Lets build some intuition for decision treeshttp://www.r2d3.us/visual-intro-to-machine-learning-part-1/
Support Vector MachineDraw a line/plane to separate the variables
Solution….
…now on to Neural Networks
http://playground.tensorflow.org/
Anomaly Detection
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Anomaly DetectionThe different types of anomaly detection schemes• Statistical based• Distance based• Model based
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Lets build some intuition
Second Attempt
®
99.9%-ile
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Exampleshttp://anomalydetection-aml.azurewebsites.net/
Is my algorithm any good?
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Accuracy is not enough
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2 metrics to rememberFALSE NEGATIVE TRUE NEGATIVE
TRUE POSITIVE
FALSEPOSITIVE
Relevant Elements
How many selected items are relevant = PRECISION
How many relevant items are Selected = RECALLSELECTED ELEMENTS
4 Step process to mastery
Step 1: Take a courseMachine Learning: Andrew NgMining Massive Datasets: Leskovec, Rajaraman, UllmanDeep learning at Oxford 2015 http://bit.ly/2ccQmnXNeural Networks for Machine Learning: G Hinton
Step 2: Get a book
Deep Learning: http://www.deeplearningbook.org/
Step 3: Do a project on KaggleBinary Classification: Titanic: Machine Learning from DisasterMulti-Class Classification: Forest Cover Type PredictionRegression with temporal component: Bike Sharing DemandBinary Classification with text data: Random Acts of PizzaSentiment Analysis: Sentiment Analysis on Movie ReviewsAudio/Video: Challenges in Representation Learning: Facial Expression Recognition ChallengeImage: The Marinexplore and Cornell University Whale Detection Challenge
Step 4: Keep yourself upto datePodcasts: Linear Digressions & Talking MachinesCheat sheets (Python/R/ML etc): http://bit.ly/2ccOQlu
Other resources:Interesting iPython Notebooks: http://bit.ly/2ccPEGZLearn Data Science http://learnds.com/A Few Useful Things to Know about Machine Learning http://bit.ly/2ccQNi5
Keep LearningYou can reach me on
Email: setu.chokshi at gmail Twitter: @setuc
Best Speaker Award from Soyoung Lee
Thank you!